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Record W2324405370 · doi:10.1242/jeb.091207

LIFE AT HIGH pH: MANAGING AMMONIA

2013· article· en· W2324405370 on OpenAlexaboutno aff
Nicola Stead

Bibliographic record

VenueJournal of Experimental Biology · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsnot available
Fundersnot available
KeywordsFish <Actinopterygii>AmmoniaGillUreaFisheryChemistryZoologyBiologyBiochemistry

Abstract

fetched live from OpenAlex

At the southernmost point of the Kenyan Rift Valley lies the inhospitable, alkaline Lake Magadi. With a pH of 10, the only fish that can survive here are Alcolapia grahami cichlids. For over two decades, Chris Wood, from McMaster University, Canada, has been intrigued by how these fish cope in their hostile habitat, and has made five expeditions to this lake to investigate. During his first expedition in 1987, Wood recalls the exciting discovery that these fish are 100% ureotelic – they only excrete their nitrogenous waste as urea. This was in stark contrast to other ammonotelic teleosts, who only excrete ammonia. But with such a high external pH, making it almost impossible to excrete ammonia, excreting urea seemed to be the only viable option for A. grahami. In more recent years, following the discovery that Rhesus (Rh) proteins facilitate ammonia transport across the gills in most fish, Wood wondered whether, in becoming ureotelic, these fish no longer had a need for Rh proteins. To find out, Wood assembled an international team of scientists and returned to Lake Magadi once more (p. 2998).From their makeshift laboratory in a nearby house, the team made early morning trips down to the lake, taking it in turns to wade into the caustic waters to capture the fish for their investigation. Upon return to the lab, Wood says: ‘We would let the fish settle down for a couple of hours and then we would put them into small chambers with an oxygen electrode so we could measure their oxygen consumption and also their urea excretion’. After a while, the team loaded the water with ammonia, and monitored the fish for 24 h. In parallel experiments, fish were regularly euthanized and the team collected blood, gill and other tissues samples for analysis back in Canada.While still in the field, the team saw that ammonia loading caused urea excretion levels to go up, but it was when they got back to Canada that the surprising results came to light. In the lab the team found that two Rh proteins, Rhbg and Rhcg2, were expressed in most tissues. Wood explains that this in itself was not entirely unexpected – the Rh proteins might be used to shuttle ammonia to other regions of the body to be converted to urea. However, he admits he was surprised to find Rh proteins in the gills. What's more, the team also found that the expression of these two Rh proteins and an ATPase essential for ammonia excretion increased after exposure to ammonia. Wood explains that increased expression of Rh proteins is a standard response in ammonotelic fish after ammonia loading; it is initiated by high cortisol levels, and sure enough, cortisol levels in the A. grahami exposed to ammonia were also raised.All the evidence suggests that even though these fish usually secrete urea, they've held onto the ammonia-sensitive ammonia excretion system. ‘Maybe when they're really challenged by high ammonia the urea mechanism is not sufficient and they might have to start pumping out ammonia as well’, speculates Wood. He adds, ‘This is quite relevant because there are areas of this lake where there are a lot of flamingos and they produce guano, and bacteria degrade the uric acid to produce a lot of ammonia in the water.’ However, quite how A. grahami does this against an ammonia gradient remains to be seen, although Wood suspects that the Rh proteins might pump ammonia into the gill cells to such an extent that it's at a higher concentration internally than externally. Wood is currently awaiting permission to return to the alkaline lake to find the answer.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0030.004
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0080.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.247
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

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